2020/05/19 by Joseph Ross, Ross, Joseph
Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning and Algorithms #Machine Learning and Data Classification #Numerical Methods and Algorithms
paper · pdf · doi:10.48550/arxiv.2005.09599
openalex publication_date 2020/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The t-digest is a data structure that can be queried for approximate quantiles, with greater accuracy near the minimum and maximum of the distribution. We develop a t-digest variant with accuracy asymmetric about the median, thereby making possible alternative tradeoffs between computational resources and accuracy which may be of particular interest for distributions with significant skew. After establishing some theoretical properties of scale functions for t-digests, we show that a tangent line construction on the familiar scale functions preserves the crucial properties that allow t-digests to operate online and be mergeable. We conclude with an empirical study demonstrating the asymmetric variant preserves accuracy on one side of the distribution with a much smaller memory footprint.